
"""simple docstring"""



import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

a       :						Optional[Any]       		=		imread(r'''digital_image_processing/image_data/lena_small.jpg''')
a       :						List[Any]       		=		cvtColor(img, COLOR_BGR2GRAY)


def 			_UpperCamelCase						(    )     ->	str:





    """simple docstring"""

    _UpperCAmelCase        = cn.convert_to_negative(_A   )
    # assert negative_img array for at least one True
    assert negative_img.any()


def 			_UpperCamelCase						(    )     ->	List[str]:





    """simple docstring"""

    with Image.open("""digital_image_processing/image_data/lena_small.jpg"""   ) as img:
        # Work around assertion for response
        assert str(cc.change_contrast(_A       ,   1_1_0   )   ).startswith(
            """<PIL.Image.Image image mode=RGB size=100x100 at"""   )


def 			_UpperCamelCase						(    )     ->	Dict:





    """simple docstring"""

    _UpperCAmelCase        = canny.gen_gaussian_kernel(9       ,   sigma=1.4   )
    # Assert ambiguous array
    assert resp.all()


def 			_UpperCamelCase						(    )     ->	Any:





    """simple docstring"""

    _UpperCAmelCase        = imread("""digital_image_processing/image_data/lena_small.jpg"""       ,   0   )
    # assert ambiguous array for all == True
    assert canny_img.all()
    _UpperCAmelCase        = canny.canny(_A   )
    # assert canny array for at least one True
    assert canny_array.any()


def 			_UpperCamelCase						(    )     ->	Union[str, Any]:





    """simple docstring"""

    assert gg.gaussian_filter(_A       ,   5       ,   sigma=0.9   ).all()


def 			_UpperCamelCase						(    )     ->	int:





    """simple docstring"""

    _UpperCAmelCase        = array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]   )
    _UpperCAmelCase        = conv.img_convolve(_A       ,   _A   ).astype(_A   )
    assert res.any()


def 			_UpperCamelCase						(    )     ->	str:





    """simple docstring"""

    assert med.median_filter(_A       ,   3   ).any()


def 			_UpperCamelCase						(    )     ->	str:





    """simple docstring"""

    _UpperCAmelCase     ,_UpperCAmelCase        = sob.sobel_filter(_A   )
    assert grad.any() and theta.any()


def 			_UpperCamelCase						(    )     ->	List[Any]:





    """simple docstring"""

    _UpperCAmelCase        = sp.make_sepia(_A       ,   2_0   )
    assert sepia.all()


def 			_UpperCamelCase						(    _A = "digital_image_processing/image_data/lena_small.jpg"   )     ->	str:





    """simple docstring"""

    _UpperCAmelCase        = bs.Burkes(imread(_A       ,   1   )       ,   1_2_0   )
    burkes.process()
    assert burkes.output_img.any()


def 			_UpperCamelCase						(    _A = "digital_image_processing/image_data/lena_small.jpg"       ,   )     ->	Any:





    """simple docstring"""

    _UpperCAmelCase        = rs.NearestNeighbour(imread(_A       ,   1   )       ,   4_0_0       ,   2_0_0   )
    nn.process()
    assert nn.output.any()







def 			_UpperCamelCase						(    )     ->	List[Any]:





    """simple docstring"""

    _UpperCAmelCase        = """digital_image_processing/image_data/lena.jpg"""

    # Reading the image and converting it to grayscale.
    _UpperCAmelCase        = imread(_A       ,   0   )

    # Test for get_neighbors_pixel function() return not None
    _UpperCAmelCase        = 0
    _UpperCAmelCase        = 0
    _UpperCAmelCase        = image[x_coordinate][y_coordinate]

    _UpperCAmelCase        = lbp.get_neighbors_pixel(
        _A       ,   _A       ,   _A       ,   _A   )

    assert neighbors_pixels is not None

    # Test for local_binary_pattern function()
    # Create a numpy array as the same height and width of read image
    _UpperCAmelCase        = np.zeros((image.shape[0], image.shape[1])   )

    # Iterating through the image and calculating the local binary pattern value
    # for each pixel.
    for i in range(0       ,   image.shape[0]   ):
        for j in range(0       ,   image.shape[1]   ):
            _UpperCAmelCase        = lbp.local_binary_value(_A       ,   _A       ,   _A   )

    assert lbp_image.any()

"""simple docstring"""



import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
a       :						Any       		=		logging.get_logger(__name__)


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	int:





    """simple docstring"""

    _UpperCAmelCase        = RobertaPreLayerNormConfig.from_pretrained(
        _A       ,   architectures=["""RobertaPreLayerNormForMaskedLM"""]   )

    # convert state_dict
    _UpperCAmelCase        = torch.load(hf_hub_download(repo_id=_A       ,   filename="""pytorch_model.bin"""   )   )
    _UpperCAmelCase        = {}
    for tensor_key, tensor_value in original_state_dict.items():
        # The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
        if tensor_key.startswith("""roberta."""   ):
            _UpperCAmelCase        = """roberta_prelayernorm.""" + tensor_key[len("""roberta."""   ) :]

        # The original implementation contains weights which are not used, remove them from the state_dict
        if tensor_key.endswith(""".self.LayerNorm.weight"""   ) or tensor_key.endswith(""".self.LayerNorm.bias"""   ):
            continue

        _UpperCAmelCase        = tensor_value

    _UpperCAmelCase        = RobertaPreLayerNormForMaskedLM.from_pretrained(
        pretrained_model_name_or_path=_A       ,   config=_A       ,   state_dict=_A   )
    model.save_pretrained(_A   )

    # convert tokenizer
    _UpperCAmelCase        = AutoTokenizer.from_pretrained(_A   )
    tokenizer.save_pretrained(_A   )


if __name__ == "__main__":
 a       :						Tuple       		=		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--checkpoint-repo''',
     default=None,
     type=str,
     required=True,
     help='''Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 a       :						Union[str, Any]       		=		parser.parse_args()
 convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)